The temporal dynamics of self-control
Bibliographic record
Abstract
Self-control—the ability to pursue long-term goals over short-term temptations—is a critical faculty of human cognition, but the cognitive processes enabling self-control are not well understood. Traditional models have focused on impulse inhibition: effortfully inhibiting prepotent motor responses toward a temptation, yielding a stage-based evolution of choice. Other models emphasize dynamic competition between goal and temptation, yielding a more integrative evolution of choice. Although these models represent fundamentally different conceptions of self-control, current methods are inadequate for investigating real-time dynamics, leaving the question of which model best describes self-control unresolved. We investigate these models using mouse-tracking: a dynamic, real-time measure of decision-making in which we measure participants’ computer mouse movements as they navigate tradeoffs between immediate and delayed gratification (e.g., $5 today vs. $20 in 3 mo). We develop a quantitative approach that integrates the rich spatial and temporal information contained in mouse trajectories, and find evidence for both impulse inhibition and dynamic competition. Notably, impulse inhibition is less frequent, occurring in only one-quarter of choices favoring larger later rewards over smaller sooner ones. We further find substantial individual variability on who relies on impulse inhibition, with more present-biased individuals more likely to use impulse inhibition to choose larger-later options. Finally, our approach reveals the diverse variability within impulse inhibition and dynamic competition, and accounting for this variability greatly strengthened models predicting out-of-sample choices. Our findings clarify the mechanisms underlying self-control and introduce a robust tool for quantifying real-time decision-making dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".